Some attrition is normal and to be expected in research. However, the type of attrition is important because systematic bias can distort your findings. Attrition bias can lead to inaccurate results because it affects internal and/or external validity.
Placebos are used in medical research for new medication or therapies, called clinical trials. In these trials some people are given a placebo, while others are given the new medication being tested.
The purpose is to determine how effective the new medication is: if it benefits people beyond a predefined threshold as compared to the placebo, it’s considered effective.
Although there is no definite answer to what causes the placebo effect, researchers propose a number of explanations such as the power of suggestion, doctor-patient interaction, classical conditioning, etc.
Common types of selection bias are:
Bias affects the validity and reliability of your findings, leading to false conclusions and a misinterpretation of the truth. This can have serious implications in areas like medical research where new forms of treatment are being evaluated.
Response bias is a general term used to describe a number of different conditions or factors that cue respondents to provide inaccurate or false answers during surveys or interviews. These factors range from the interviewer’s perceived social position or appearance to the the phrasing of questions in surveys.
Nonresponse bias occurs when the people who complete a survey are different from those who did not, in ways that are relevant to the research topic. Nonresponse can happen because people are either not willing or not able to participate.
Observer bias occurs when the researcher’s assumptions, views, or preconceptions influence what they see and record in a study, while actor–observer bias refers to situations where respondents attribute internal factors (e.g., bad character) to justify other’s behavior and external factors (difficult circumstances) to justify the same behavior in themselves.
Research bias affects the validity and reliability of your research findings, leading to false conclusions and a misinterpretation of the truth. This can have serious implications in areas like medical research where, for example, a new form of treatment may be evaluated.
Stratified and cluster sampling may look similar, but bear in mind that groups created in cluster sampling are heterogeneous, so the individual characteristics in the cluster vary. In contrast, groups created in stratified sampling are homogeneous, as units share characteristics.
Relatedly, in cluster sampling you randomly select entire groups and include all units of each group in your sample. However, in stratified sampling, you select some units of all groups and include them in your sample. In this way, both methods can ensure that your sample is representative of the target population.
When your population is large in size, geographically dispersed, or difficult to contact, it’s necessary to use a sampling method.
This allows you to gather information from a smaller part of the population (i.e., the sample) and make accurate statements by using statistical analysis. A few sampling methods include simple random sampling, convenience sampling, and snowball sampling.
The observer-expectancy effect occurs when researchers influence the results of their own study through interactions with participants.
Researchers’ own beliefs and expectations about the study results may unintentionally influence participants through demand characteristics.
The observer-expectancy effect is often used synonymously with the Pygmalion or Rosenthal effect.
You can use several tactics to minimize observer bias.
It’s impossible to completely avoid observer bias in studies where data collection is done or recorded manually, but you can take steps to reduce this type of bias in your research.
Observer bias occurs when a researcher’s expectations, opinions, or prejudices influence what they perceive or record in a study. It usually affects studies when observers are aware of the research aims or hypotheses. This type of research bias is also called detection bias or ascertainment bias.
If you have a small amount of attrition bias, you can use some statistical methods to try to make up for it.
Multiple imputation involves using simulations to replace the missing data with likely values. Alternatively, you can use sample weighting to make up for the uneven balance of participants in your sample.
To avoid attrition, applying some of these measures can help you reduce participant dropout by making it easy and appealing for participants to stay.
Attrition bias can skew your sample so that your final sample differs significantly from your original sample. Your sample is biased because some groups from your population are underrepresented.
With a biased final sample, you may not be able to generalize your findings to the original population that you sampled from, so your external validity is compromised.
Attrition bias is a threat to internal validity. In experiments, differential rates of attrition between treatment and control groups can skew results.
This bias can affect the relationship between your independent and dependent variables. It can make variables appear to be correlated when they are not, or vice versa.
Attrition bias is the selective dropout of some participants who systematically differ from those who remain in the study.
Some groups of participants may leave because of bad experiences, unwanted side effects, or inadequate incentives for participation, among other reasons. Attrition is also called subject mortality, but it doesn’t always refer to participants dying!
You can control demand characteristics by taking a few precautions in your research design and materials.
Use these measures:
Demand characteristics are a type of extraneous variable that can affect the outcomes of the study. They can invalidate studies by providing an alternative explanation for the results.
These cues may nudge participants to consciously or unconsciously change their responses, and they pose a threat to both internal and external validity. You can’t be sure that your independent variable manipulation worked, or that your findings can be applied to other people or settings.
In research, demand characteristics are cues that might indicate the aim of a study to participants. These cues can lead to participants changing their behaviors or responses based on what they think the research is about.
Demand characteristics are common problems in psychology experiments and other social science studies because they can cause a bias in your research findings.
Sampling bias occurs when some members of a population are systematically more likely to be selected in a sample than others.
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